



'''simple docstring'''






from pathlib import Path

import numpy as np
from PIL import Image


def 					snake_case_	(   _lowerCAmelCase    :		Tuple    ) ->  np.ndarray:
							UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase					:			int									=						rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
							return 0.2_9_8_9 * r + 0.5_8_7_0 * g + 0.1_1_4_0 * b


def 					snake_case_	(   _lowerCAmelCase    :		str    ) ->  np.ndarray:
							return (gray > 127) & (gray <= 255)


def 					snake_case_	(   _lowerCAmelCase    :		Dict  , _lowerCAmelCase    :		Any    ) ->  np.ndarray:
							UpperCAmelCase					:			Tuple									=						np.zeros_like(a__    )
							UpperCAmelCase					:			Union[str, Any]									=						np.zeros(
							    (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)    )

							# Copy image to padded image
							UpperCAmelCase					:			Tuple									=						image

							# Iterate over image & apply kernel
							for x in range(image.shape[1]    ):
														for y in range(image.shape[0]    ):
																					UpperCAmelCase					:			Dict									=						(
																					    kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
																					).sum()
																					UpperCAmelCase					:			Optional[Any]									=						int(summation > 0    )
							return output


if __name__ == "__main__":
						# read original image
						UpperCamelCase__:  Optional[Any]				      =     Path(__file__).resolve().parent / "image_data" / "lena.jpg"
						UpperCamelCase__:  Dict				      =     np.array(Image.open(lena_path))
						# kernel to be applied
						UpperCamelCase__:  Dict				      =     np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
						UpperCamelCase__:  Tuple				      =     dilation(gray_to_binary(rgb_to_gray(lena)), structuring_element)
						# Save the output image
						UpperCamelCase__:  Dict				      =     Image.fromarray(output).convert("RGB")
						pil_img.save("result_dilation.png")






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowerCamelCase__  =					{
    '''configuration_wav2vec2''': ['''WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Wav2Vec2Config'''],
    '''feature_extraction_wav2vec2''': ['''Wav2Vec2FeatureExtractor'''],
    '''processing_wav2vec2''': ['''Wav2Vec2Processor'''],
    '''tokenization_wav2vec2''': ['''Wav2Vec2CTCTokenizer''', '''Wav2Vec2Tokenizer'''],
}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__  =					[
       '''WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''Wav2Vec2ForAudioFrameClassification''',
       '''Wav2Vec2ForCTC''',
       '''Wav2Vec2ForMaskedLM''',
       '''Wav2Vec2ForPreTraining''',
       '''Wav2Vec2ForSequenceClassification''',
       '''Wav2Vec2ForXVector''',
       '''Wav2Vec2Model''',
       '''Wav2Vec2PreTrainedModel''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__  =					[
       '''TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFWav2Vec2ForCTC''',
       '''TFWav2Vec2Model''',
       '''TFWav2Vec2PreTrainedModel''',
       '''TFWav2Vec2ForSequenceClassification''',
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__  =					[
       '''FlaxWav2Vec2ForCTC''',
       '''FlaxWav2Vec2ForPreTraining''',
       '''FlaxWav2Vec2Model''',
       '''FlaxWav2Vec2PreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
   from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
   from .processing_wavaveca import WavaVecaProcessor
   from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_wavaveca import (
          WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
          WavaVecaForAudioFrameClassification,
          WavaVecaForCTC,
          WavaVecaForMaskedLM,
          WavaVecaForPreTraining,
          WavaVecaForSequenceClassification,
          WavaVecaForXVector,
          WavaVecaModel,
          WavaVecaPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_wavaveca import (
          TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFWavaVecaForCTC,
          TFWavaVecaForSequenceClassification,
          TFWavaVecaModel,
          TFWavaVecaPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_wavaveca import (
          FlaxWavaVecaForCTC,
          FlaxWavaVecaForPreTraining,
          FlaxWavaVecaModel,
          FlaxWavaVecaPreTrainedModel,
      )


else:
   import sys

   lowerCamelCase__  =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




